Identification of Mode Switching Condition in Overtaking Behavior Using Variable-Free Logistic Regression Model

Okuda, Hiroyuki; Yoshino, Soichi; Suzuki, Tatsuya; Kawai, Toshitake

doi:10.1109/itsc.2015.251

Cited by 2 publications

(5 citation statements)

References 14 publications

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“…Therefore, the new problem must be addressed as how the driver's gazing behavior can be included in the model. In order to consider the driver's gazing behavior, this paper proposes an extended modeling strategy of [34] as shown in Fig. 1, which consist of the system identification process and evaluation of the identified model, where the line-of-sight information for estimating driver's gazing behavior is included in the modeling of driver's decision in the overtaking driving behavior.…”

Section: System Identification Process Model Evaluationmentioning

confidence: 99%

“…Since the logistic regression models are connected linearly, there was a concern that if the estimation rate of one mode is relatively bad, the estimation rate of the next mode would also be affected. However, judging from Comparing the results (Table 6 -8) of the work presented here to the results (Table 9) of the previous work [34], where the experiment setting and drivers were the same and only the surrounding vehicles' information was collected and used to model the driver's overtaking decision making. In this study, by including the gazing behavior information, there was improvement in the correct switching rate for all the drivers in all the modes defined for the overtaking driving task.…”

Section: Model Verification Based On Estimation Performancementioning

confidence: 99%

“…Based on this background, the authors have proposed a new approach to model the mode switching condition in the driving behavior without any prior knowledge about the selection of the input variables in [34]. According to the result by Okuda et al (2015) [34], a logistic regression model was successfully demonstrated to reproduce the mode switching in driving behaviors. The logistic regression model can represent a relationship between the continuous/discrete input variables and the binary output variable.…”

Section: Introductionmentioning

confidence: 99%

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Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

Nwadiuto

Yoshino²,

Okuda

et al. 2021

IEEE Access

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This paper investigates the decision-making characteristics of the driver in the overtaking on the highway road. For the research purpose, a novel method was proposed by introducing a logistic regression model accompanied by the statistical test technique, which does not require prior knowledge about the explanatory variables. This study hypothesizes that the driver's gazing behavior is crucial for the decision-making process in driving and hence, the line-of-sight information was introduced to estimate driver's gazing behavior in the model of driver's decision specifically for reproducing the overtaking driving behavior accurately. Consequently, the proposed model realized a high describability on the decision of the driver when performing the overtaking driving task, which is one of the significant advancements of the present study with respect to the past similar studies. This study integrates the perspectives of intelligent vehicle design and cognitive science by revealing which factor the driver pays attention to in a changeable driving environment due to various observable factors. In experiments based on the driving simulator with six human subjects, the overtaking behavior was successfully estimated by specifying a set of variables to reconstruct the driver's behavior and then the proposed model provided a minimum set of necessary variables accompanied with key coefficients. In conclusion, the proposed approach based on a simple logistic regression model demonstrated driving behaviors with an accurate estimation of the driver's intention without the need for prior knowledge, and it may contribute to higher describability for various driving actions in a dynamic environment.

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Section: System Identification Process Model Evaluationmentioning

confidence: 99%

Section: Model Verification Based On Estimation Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

Nwadiuto

Yoshino²,

Okuda

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…One big challenge is how to define principal features for different drivers, because different drivers have different driving characteristics and therefore useful features for different drivers may be entirely different. Inspired by [73,74], the principal individual driving characteristics can be extracted by using model selection techniques (e.g. Wald statistics) [73] or feature selection algorithms (e.g.…”

Section: A Adaptive Cruise Controlmentioning

confidence: 99%

“…Inspired by [73,74], the principal individual driving characteristics can be extracted by using model selection techniques (e.g. Wald statistics) [73] or feature selection algorithms (e.g. sequential forward floating selection) [74].…”

Section: A Adaptive Cruise Controlmentioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

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Identification of Mode Switching Condition in Overtaking Behavior Using Variable-Free Logistic Regression Model

Cited by 2 publications

References 14 publications

Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

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